1. Research Questions.
   • What are the spatial patterns of western juniper within the treated watershed? (The project was later expanded to address juniper density within the treated watershed, untreated watershed, and in nearby areas.)
   • What are the spatial patterns of western juniper within the study area as they relate to the spatial pattern of slope and/or aspect (as potential indicators of soil moisture) and the density of juniper in a given area (as a potential indicator of seed source)?
2. Description of dataset.
   • The study took place in the Camp Creek Paired Watershed Study (CCPWS), located in central OR. The majority of western juniper was removed from one watershed in 2005-2006 (“treated watershed), while mature western juniper is the dominant vegetation at the other watershed (“untreated watershed”).
   • Thirty-three belt transects (3m by 30m) within the treated watershed recorded the location and size of juniper within each transect.
   • Multispectral imagery were collected using a UAV for a subset of the treated watershed. Brightness values for red, green, blue, red-edge, near infrared wavelengths were used. Resolution is approximately 4 cm.
   • National Agricultural Imagery Program (NAIP) imagery from 2016 was used for the study area. Resolution of this dataset is 1 m.
   • A 30 m Digital Elevation Model (DEM) was used to determine the slope and aspect characteristics within the study area and develop a rating model as an indicator of potential soil moisture. Slope and aspect were each rated from 1-9, with areas associated with higher soil moisture (northerly aspects, flat slopes) being assigned higher values.
   • All data was projected to NAD 1983 UTM Zone 10N.
   • Supervised classification (support vector machine) was used to identify juniper within the NAIP and UAV imagery.
3. Hypotheses.
   • I expect juniper density will be dispersed unevenly throughout the study areas based on soil moisture, with high density patches of juniper being found in areas with northern aspects and/or flatter slopes. While juniper can survive in dry, rocky areas we would expect greater numbers where higher soil moisture allows for improved survival and growth. It is anticipated that patches of highest juniper density will be found in those areas with presumed highest soil moisture values based on slope and aspect characteristics, with northerly aspects and flat slopes presumed to have highest soil moisture values and areas of steep slopes with southerly aspect presumed to have the lowest soil moisture. Assuming similar soil depth and type, higher soil moisture is anticipated on northern-facing slopes compared to southerly-facing slopes because the intensity and amount of sunlight received is lower on northern slopes (in the northern hemisphere) resulting in lower soil evaporation. Higher soil moisture is presumed on flatter slopes because infiltration is assumed to be higher in these areas while higher rates of runoff are assumed on steeper slopes.
   • It is anticipated that juniper density (mature and sapling) will be higher in areas where there are higher levels of seed sources (as indicated by the juniper density surrounding each point of interest analyzed). Specifically, areas of higher density juniper saplings may be expected near higher densities of mature juniper (such as those areas near the watershed boundary). The seeds of juniper may be spread through a number of means, to include birds, winds, or small mammals.
4. Approaches.
   • Kriging and inverse distance weighted (IDW) interpolation were used with the belt transect data (represented as individual points at the start of each transect).
   • Spatial autocorrelation (Global Moran’s I) was calculated using the belt transect data (using number of juniper per transect). Spatial autocorrelation was also assessed for juniper density as it related to slope and aspect characteristics.
   • Hot spot analysis (Getis-Ord Gi) was used to assess area of juniper concentration.
   • Juniper density surrounding specific points/areas (based on NAIP imagery) was analyzed.
   • Ordinary least squares (OLS) and geographically weighted regression (GWR) were used to assess juniper density as it corresponded to slope and aspect characteristics.
   • A chi-squared test and logistic model were used to assess the rating model and classified NAIP rasters.
5. Results.
   • Based on the interpolation techniques (kriging and IDW) used areas of high and low density juniper patches were indicated within the treated watershed. However, the locations of these patches did not correspond to regions where we expected high or low soil moisture. For example, areas of highest densities of juniper density based on the transects were found in southeastern and western areas of the watershed. This may be attributed to the small dataset (n=33) used for this analysis but may also suggest that other processes (such as seed distribution via birds or the presence of other vegetation/ground cover) may influence juniper density.One hot spot was indicated using the belt transects with northwestern aspect and 8.8% slope and one cold spot was indicated with north-northwestern aspect and a 11.5% slope.
   • The hot spot analysis based on the UAV imagery indicated four hot spots, with slope values ranging from 3-11% and different aspect characteristics (northwestern, southwestern, southwestern, and southeastern).
   • The initial GWR and OLS analysis did not find that slope category and/or aspect category were good explanatory variables for juniper density based on the belt transects, based on adjusted R-squared values ranging from 0.19 to 0.28.
   • A weakly significant relationship was indicated between the rating model and the classified NAIP raster (p=0.018).
   • In order to address issues with limited variance encountered in earlier analysis, an additional GWR was conducted comparing the juniper density in a 150m buffer around 84 random points to the rating model. Outside of the buffer size, methods used in this GWR are similar to those described in previous exercises.  This improved the adjusted R-squared value for this model to 0.53.
   • 
   • The results of this analysis largely did not support the proposed hypotheses that greater juniper density would be associated with topographic characteristics associated with higher soil moisture. In situ soil moisture measurements were not used in this analysis, so differences in anticipated soil moisture associated with slope and aspect could not be compared to actual soil moisture measurements.
   • Greater densities of western juniper are evident in the untreated watershed compared to the treated watershed (as expected). However, patterns of juniper distribution within the treated watershed (as indicated by the belt transects) did not show any correlation between slope and aspect and areas of higher or lower juniper density. Additionally, those areas closest to the watershed boundary (and therefore closest to areas of dense, mature juniper) did not have greater juniper density than areas further away from the watershed boundary.
6. Significance. 
   • While results of this analysis are of limited use to land managers at this point, a similar approach with an expanded dataset may improve is utility. The use of higher resolution imagery (such as UAV imagery) may improve the identification of juniper saplings and the reliability of this analysis. The NAIP imagery used in this analysis may be sufficient for identifying mature juniper, but small (sub-meter) juniper saplings may not be accurately detected and/or misidentified. Additionally, more ground-based data (additional belt transects, etc. distributed throughout both watersheds) could provide greater information about juniper density at this study site.
   • Past research has found that juniper encroachment is associated with reduced undercanopy soil moisture [1]. The focus of this analysis was on the relationship between slope and aspect (as indicators of soil moisture) and juniper density, but further analysis may focus on how soil moisture varies with juniper density. Additionally, this analysis could also assess non-juniper vegetation density as it relates to juniper density, soil moisture, and other watershed characteristics.
   • The results suggest that other factors may be influencing juniper density in addition to slope and aspect characteristics but further analysis is needed. It should also be noted that historically western juniper was largely found in rocky outcroppings and other areas protected from fire. However, fire suppression and other factors such as livestock grazing and climate change may be contributing to an increase of juniper density [2]. Future analysis may also focus on how these factors (e.g., disturbance related to land use, etc.) are related to juniper density over larger scales.
7. My learning: Software.
   • Some difficulty encountered working when comparing raster and vector data.
   • Due to limited data set and differences in resolution, limited conclusions could be drawn from data. However, the methods could be applied to other datasets.
   • Some difficulty was encountered using ArcGIS 10.6/Pro, but issues were resolved by switching between versions.
   • Once packages and data was added, raster analysis in R-studio was relatively simple and efficient for this analysis. The R-bridge could potentially simplify this process allowing for more complicated analysis.
   • I was able to reinforce some basic skills in R and ArcGIS as well as gain exposure to new tools (such as the Ripey’s K and hot spot analysis). In particular, I was able to gain more exposure to using model builder in ArcGIS Pro for extracting and analyzing that would be time consuming and inefficient to perform otherwise.
8. My learning: Statistics.
   • I did not have previous experience with several techniques used in this analysis, to include hot spot analysis and OLS/GWR. This analysis demonstrated how these methods could be applied in the future, as well as potential limitations (e.g., small datasets, clustered data, etc.). I would be interested in applying the hot spot analysis in particular to a larger data set and to higher resolution imagery for analyzing vegetation density.
   • I have used several kriging approaches and IDW to a limited degree in the past. Similar limitations to those cited above were experienced but this is an approach that may have applicability that for in situ soil moisture measurements in the watershed.
   • In addition to those methods used in this analysis, the tutorials helped reinforce concepts related to other methods, to include: principal component analysis, Ripley’s K, and qualitative analysis using maps.
   • A major takeaway of this analysis was the potential ways that different statistical approaches can be combined to perform analysis (e.g. using a combination of OLS, Moran’s I, and GWR, etc.).

References

1. Lebron, I.; Madsen, M.D.; Chandler, D.G.; Robinson, D.A.; Wendroth, O.; Belnap, J. Ecohydrological controls on soil moisture and hydraulic conductivity within a pinyon-juniper woodland. Water Resour. Res. 2007, 43, W08422.
2. Soulé, P.T.; Knapp, P.A.; Grissino-Mayer, H.D. Human Agency, Environmental Drivers, and Western Juniper Establishment During the Late Holocene. Ecol. Appl. 2004, 14, 96–112.